In [1]:
import plotly.graph_objs as go
import pandas as pd
from plotly.offline import iplot
In [2]:
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
df.head(5)
Out[2]:
Date AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted dn mavg up direction
0 2015-02-17 127.489998 128.880005 126.919998 127.830002 63152400 122.905254 106.741052 117.927667 129.114281 Increasing
1 2015-02-18 127.629997 128.779999 127.449997 128.720001 44891700 123.760965 107.842423 118.940333 130.038244 Increasing
2 2015-02-19 128.479996 129.029999 128.330002 128.449997 37362400 123.501363 108.894245 119.889167 130.884089 Decreasing
3 2015-02-20 128.619995 129.500000 128.050003 129.500000 48948400 124.510914 109.785449 120.763500 131.741551 Increasing
4 2015-02-23 130.020004 133.000000 129.660004 133.000000 70974100 127.876074 110.372516 121.720167 133.067817 Increasing
In [3]:
data = [go.Scatter(
          x=df.Date,
          y=df['AAPL.Close'])]

iplot(data)
In [4]:
tesla = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/tesla-stock-price.csv")
tesla.head(5)
Out[4]:
date close volume open high low
0 11:34 270.49 4,787,699 264.50 273.88 262.2400
1 2018/10/15 259.59 6189026.0000 259.06 263.28 254.5367
2 2018/10/12 258.78 7189257.0000 261.00 261.99 252.0100
3 2018/10/11 252.23 8128184.0000 257.53 262.25 249.0300
4 2018/10/10 256.88 12781560.0000 264.61 265.51 247.7700
In [5]:
trace_one = go.Scatter(
            x=tesla.date,
            y=tesla['high'],
            name= "Tesla High",
            line = dict(color='#17BECF'),
            opacity = 0.8)

trace_two = go.Scatter(
            x=tesla.date,
            y=tesla['low'],
            name= "Tesla Low",
            line = dict(color='#7F7F7F'),
            opacity = 0.8)

data = [trace_one, trace_two]


layout = dict(
        title = 'Tesla Stock Price - High vs Low')


fig = dict(data=data, layout=layout)
iplot(fig, filename = 'Tesla Stock Comparison')
In [ ]: